[R] lme model specification

[Spencer Graves]

	  Does each subject get only one LED per session or all 4 LEDs?  This 
should be important regarding which models are estimaable.  In either 
case, might the following help you?

nSubj <- 8
nSess <- 4
nObsPerSess <- 3

library(nlme)
library(e1071)
P4 <- permutations(4)

LED <- letters[t(P4[permSubj,])]

set.seed(1)
permSubj <- sample(24, nSubj)
N <- nSubj*nSess*nObsPerSess
DF <- data.frame(
   Subject=rep(1:nSubj, each=nSess*nObsPerSess),
   illum=rep(c("star", "moon"), each=N/2),
   feedback=rep(c("yes", "no"), each=N/4, length=N),
   session=rep(1:nSess, each=nObsPerSess, nSubj),
   LED=rep(LED, each=nObsPerSess),
   Rep=rep(1:nObsPerSess, nSess*nSubj),
   logdistance=rep(1:nObsPerSess, nSess*nSubj),
   logestimate=rnorm(nSubj*nSess*nObsPerSess) )

fit <- lme(logestimate~logdistance*illum*feedback+LED,
   random=~1|Subject,
   correlation=corAR1(form=~Rep|Subject/session),
   data=DF)

	  spencer graves

Bill Simpson wrote:

> I have been asked to analyse the results of (what is to me) a very
> complicated experiment.
> 
> The dependent measure is the estimated distance, which is measured as a
> function of the actual distance. There are also several other IVs.
> 
> The plot of log estimated distance as a function of log distance is
> linear. So in the rest of the analysis I will use logestimate and
> logdistance.
> 
> My plan is to see how the other IVs affect the slope and intercept of
> this linear relationship between logestimate and log distance.
> 
> What complicates everything is that each datum point is not independent.
> Rather, many data points come from each subject.
> 
> So:
> * Each subject gets many objects at many distances which he has to
> estimate.
> * Each subject repeats this experiment using 4 colours of LEDs.
> * Each subject repeats this experiment on 4 different sessions.
> * Half the subjects do this under starlight, half under moonlight.
> * Half the subjects do it with feedback and half without.
> 
> So some of these variables are within subjects and some between. I think
> lme is a good way to proceed. But I am hung up on how to specify the
> model
> 
> fit<-lme(fixed=logestimate~logdistance*session*illum*feedback,
> random=???|subject???, data=df1)
> 
> I am familiar with the steps of model building using lm(), exploring
> different models etc, so I think I will be OK once I get the idea of
> specifying the basic lme model.
> 
> I have Pinheiro and Bates (2000) here.
> 
> Thanks very much for any help
> 
> Bill Simpson
> 
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